Short term load forecasting by artificial neural network

Ray, Papia; Mishra, Debasis; Lenka, Rajesh Kumar

doi:10.1109/icngis.2016.7854003

Cited by 28 publications

(8 citation statements)

References 12 publications

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“…The authors of [26,27] utilized Equation (6) to give the inertia weight w k on each iteration of swarm:…”

Section: Overview Of Pso Algorithmsmentioning

confidence: 99%

“…Thus, we propose a solution to overcome this disadvantage. In the first 100 iterations, the value of w decreases from 0.9 to 0.4 according to Equation (6). From Iteration 101, the value of w remains equal to 0.9.…”

Section: Comparison Between Pso and Spsomentioning

confidence: 99%

“…The Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are applied separately to train artificial neural networks (ANNs). The integration of GA into STLF is published in many papers (e.g., [2][3][4][5][6][7][8]). In these studies, the authors carefully used the most suitable parameters of GA to improve the precision.…”

Section: Introductionmentioning

confidence: 99%

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Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam

Pham

Nguyen

et al. 2019

Energies

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Recently, power companies apply optimal algorithms for short-term load forecasting, especially the daily load. However, in Vietnam, the load forecasting of the power system has not focused on this solution. Optimal algorithms and can help experts improve forecasting results including accuracy and the time required for forecasting. To achieve both goals, the combinations of different algorithms are still being studied. This article describes research using a new combination of two optimal algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This combination limits the weakness of the convergence speed of GA as well as the weakness of PSO that it easily falls into local optima (thereby reducing accuracy). This new hybrid algorithm was applied to the Southern Power Corporation’s (SPC—a large Power company in Vietnam) daily load forecasting. The results show the algorithm’s potential to provide a solution. The most accurate result was for the forecasting of a normal working day with an average error of 1.15% while the largest error was 3.74% and the smallest was 0.02%. For holidays and weekends, the average error always approximated the allowable limit of 3%. On the other hand, some poor results also provide an opportunity to re-check the real data provided by SPC.

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“…The authors of [26,27] utilized Equation (6) to give the inertia weight w k on each iteration of swarm:…”

Section: Overview Of Pso Algorithmsmentioning

confidence: 99%

Section: Comparison Between Pso and Spsomentioning

confidence: 99%

See 1 more Smart Citation

Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam

Pham

Nguyen

et al. 2019

Energies

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“…DL models are especially suitable for big-data temporal sequences due to their capacity to extract complex patterns automatically without feature extraction pre-processing steps [7]. As an evolution from simple ANNs, deep fully connected networks have been applied for load forecasting problems [8]. However, fully connected networks are unable to capture the • A temporal convolutional neural network model to achieve high accuracy in forecasting over energy demand time series • A thorough experimental study, comparing the performance of temporal convolutional with long short-term memory networks for time series forecasting…”

Section: Introductionmentioning

confidence: 99%

Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

Lara-Benítez¹,

Carranza-García²,

Luna-Romera³

et al. 2020

Preprint

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Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these type of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand, and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

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“…Through these applications, the study found that the average relative error of the two single models was 15% and 16%, respectively. Ray et al [41] also used genetic algorithms and neural networks to predict electrical load, and found that genetic algorithms provide better prediction results than backpropagation.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models

Yang

2019

Sustainability

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India's coal consumption is closely related to greenhouse gas emissions and the balance of supply and demand in energy trading markets. Most existing research on India focuses on total energy, renewable energy and energy intensity. To fill this gap, this study used two single forecasting models: the metabolic grey model (MGM) and the Back-Pro-Pagation Network (BP) to make predictions. In addition, based on these two single models, this study also developed the ARIMA correction principle and derived two combined models: the metabolic grey model, the Autoregressive Integrated Moving Average model (MGM-ARIMA) and Back-Pro-Pagation Network; and the Autoregressive Integrated Moving Average model (BP-ARIMA). After fitting India's coal consumption during 1995-2017, the average relative errors of the four models were 2.28%, 1.53%, 1.50% and 1.42% respectively. The forecast results show that coal consumption in India will continue to increase at an average annual rate of 2.5% during the period from 2018-2030.

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Short term load forecasting by artificial neural network

Cited by 28 publications

References 12 publications

Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam

Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam

Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting

Forecasting Coal Consumption in India by 2030: Using Linear Modified Linear (MGM-ARIMA) and Linear Modified Nonlinear (BP-ARIMA) Combined Models

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